1. Field of the Invention
The present invention relates generally to acoustic noise suppression systems, and, more particularly, to a novel technique for automatically selecting gain parameters for a noise suppression system employing spectral subtraction.
2. Description of the Prior Art
The primary objective of acoustic noise suppression systems is to improve the overall quality of speech. The addition of noise suppression to a speech communication system enhances speech intelligibility by filtering environmental background noise from the desired speech signal. This speech enhancement process is particularly necessary in environments having abnormally high levels of ambient background noise, such as a noisy factory, an aircraft, or a moving vehicle.
Numerous approaches have been proposed for enhancement of speech that has been degraded by ambient background noise. An overview of these techniques may be found in J. S. Lim and A. V. Oppenheim, "Enhancement and Bandwidth Compression of Noisy Speech," Proc. IEEE, vol. 67, no. 12 (December 1979), pp. 1586-1604. One very sophisticated technique, described therein, is the process of spectral subtraction. In this approach, the entire input signal spectrum is divided by a bank of bandpass filters, and particular spectral bands (corresponding to the filtered output signals) exhibiting relatively low signal-to-noise ratios (SNRs) are attenuated. All of the spectral bands, including both the attenuated bands and those bands which were not affected due to the their high SNRs, are then recombined to produce the noise-suppressed output signal
Several modifications to the basic spectral subtraction noise suppression technique have been described in the prior art. For example, R. J. McAulay and M. L. Malpass, in the article "Speech Enhancement Using a Soft-Decision Noise Suppression Filter," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-28, no. 2, (April 1980), pp. 137-145, propose a two-state soft-decision maximum-liklihood algorithm which results in a class of various noise suppression curves. In terms of a noise suppression prefilter, these curves determine the amount of suppression applied to a particular frequency channel by utilizing the measured SNR as a pointer for a look-up table to determine the attenuation for that particular spectral band. In other words, the noise suppression gain parameter is determined as a function of the individual channel number and the estimated signal-to-noise ratio.
Alternative methods for determining the noise suppression gain factors are described by Kates, in U.S. Pat. No. 4,454,609 and by Graupe et. al., in U.S. Pat. No. 4,185,168. Kates describes a combinational logic matrix providing weighting factors based upon certain combinations of the envelope-detected input signal energies and empirically-determined constant coefficients. These weights are then compared to a preselected threshold, and a gain factor is selected. Graupe describes an adaptive filter wherein the gain-to-noise parameter relationship approximates that of a Weiner or Kalman filter. Again, the gain parameters are selected as a function of the amount of detected energy in a particular band of input signal.
However, in specialized applications involving abnormally high background noise levels, even the more sophisticated noise suppression techniques become ineffective. One example of such application is the vehicle speakerphone option to a cellular mobile radio telephone system which provides hands-free operation for the automobile driver. The mobile hands-free microphone is typically located at a greater distance from the user, such as being mounted overhead on the visor. The more distant microphone delivers a much poorer signal-to-noise level to the land-end party due to road and wind noise conditions. Although the received speech signal at the land-end is usually intelligible, continuous exposure to such background noise levels often increases listener fatigue.
Although most prior art techniques perform sufficiently well under nominal background noise conditions, the performance of these approaches becomes severely limited when used in such specialized applications of unusually high background noise. Typical spectral subtraction noise suppression systems may reduce the background noise level over the voice frequency spectrum by as much as 10 dB without seriously affecting the speech quality. However, when these prior art techniques are used in relatively high background noise environments requiring noise suppression levels approaching 20 dB, there is a substantial degradation in the quality characteristics of the voice. Furthermore, in rapidly-changing high noise environments, a severe low frequency noise flutter develops in the output speech signal. This noise flutter is inherent to a spectral subtraction noise suppression system, since the individual channel gain parameters are continuously being updated in response to the changing background noise environment.
Hence, acoustic noise suppression systems usually represent a substantial compromise between noise suppression depth and distortion of the desired speech signal. A need, therefore, exists for an improved method and means for selecting noise suppression gain parameters adapted for use in high ambient noise environments without compromising voice quality